Despite the decent performance exhibited by these neural coreference models, there are still many remaining challenges. One is that there may exist many varieties for a single entity's mentions, and it is very unlikely that our training data can explicitly label all of these singleton mentions. Exiting models suffer from mention proposal [1]}. Also most of the neural-based models are effective at identifying and clustering mentions in a document context which are similar to that in the training dataset, but perform much worse once the mention's document context changes. This is primarily because most neural-based models are trained to understand the mention links only using the documents given by the training dataset. Once the document contexts change a lot and are very different from what the systems have seen in the training dataset, the model cannot link the coreferent mentions or entities correctly base on what the system learnt from training data.
